Search results matching tag 'SQL Server'http://sqlblog.com/search/SearchResults.aspx?o=DateDescending&tag=SQL+Server&orTags=0Search results matching tag 'SQL Server'en-USCommunityServer 2.1 SP2 (Build: 61129.1)Data Mining Algorithms – Hierarchical Clusteringhttp://sqlblog.com/blogs/dejan_sarka/archive/2015/03/28/data-mining-algorithms-hierarchical-clustering.aspxSat, 28 Mar 2015 06:33:25 GMT21093a07-8b3d-42db-8cbf-3350fcbf5496:58301Dejan Sarka<p>Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. Dissimilarities are assessed based on the attribute values describing the objects.</p> <p>There are a large number of clustering algorithms. The major methods can be classified into the following categories.</p> <ul> <li><strong>Partitioning methods</strong>. A partitioning method constructs K partitions of the data, which satisfy the following requirements: (1) each group must contain at least one object and (2) each object must belong to exactly one group. Given the initial K number of partitions to construct, the method creates initial partitions. It then uses an iterative relocation technique that attempts to improve the partitioning by moving objects from one group to another. There are various kinds of criteria for judging the quality of the partitions. Some most popular include the k-means algorithm, where each cluster is represented by the mean value of the objects in the cluster, and the k-medoids algorithm, where each cluster is represented by one of the objects located near the center of the cluster.</li> <li><strong>Hierarchical methods</strong>. A hierarchical method creates a hierarchical decomposition of the given set of data objects. These methods are agglomerative or divisive. The agglomerative (bottom-up) approach starts with each object forming a separate group. It successively merges the objects or groups close to one another, until all groups are merged into one. The divisive (top-down) approach starts with all the objects in the same cluster. In each successive iteration, a cluster is split up into smaller clusters, until eventually each object is in one cluster or until a termination condition holds.</li> <li><strong>Density-based methods</strong>. Methods based on the distance between objects can find only spherical-shaped clusters and encounter difficulty in discovering clusters of arbitrary shapes. So other methods have been developed based on the notion of density. The general idea is to continue growing the given cluster as long as the density (number of objects or data points) in the “neighborhood” exceeds some threshold; that is, for each data point within a given cluster, the neighborhood of a given radius has to contain at least a minimum number of points.</li> <li><strong>Model-based methods</strong>. Model-based methods hypothesize a model for each of the clusters and find the best fit of the data to the given model. A model-based technique might locate clusters by constructing a density function that reflects the spatial distribution of the data points. Unlike conventional clustering, which primarily identifies groups of like objects, this conceptual clustering goes one step further by also finding characteristic descriptions for each group, where each group represents a concept or a class.</li> </ul> <p>A hierarchical clustering model training typically starts by calculating a <strong>distance matrix</strong> – a matrix with distances between data points in a multidimensional hyperspace, where each input variable defines one dimension of that hyperspace. Distance measure can be a geometrical distance or some other, more complex measure. A <strong>dendrogram</strong> is a tree diagram frequently used to illustrate the arrangement of the clusters produced by hierarchical clustering. Dendrograms are also often used in computational biology to illustrate the clustering of genes or samples. The following set of pictures shows the process of building an agglomerative hierarchical clustering dendrogram.</p> <p><a href="http://sqlblog.com/blogs/dejan_sarka/image_5B43D530.png"><img title="image" style="border-top:0px;border-right:0px;border-bottom:0px;border-left:0px;display:inline;" border="0" alt="image" src="http://sqlblog.com/blogs/dejan_sarka/image_thumb_246980EA.png" width="202" height="244" /></a> <a href="http://sqlblog.com/blogs/dejan_sarka/image_1F86CD2E.png"><img title="image" style="border-top:0px;border-right:0px;border-bottom:0px;border-left:0px;display:inline;" border="0" alt="image" src="http://sqlblog.com/blogs/dejan_sarka/image_thumb_1F1A9A39.png" width="136" height="52" /></a><a href="http://sqlblog.com/blogs/dejan_sarka/image_339FE9B7.png"><img title="image" style="border-top:0px;border-right:0px;border-bottom:0px;border-left:0px;display:inline;" border="0" alt="image" src="http://sqlblog.com/blogs/dejan_sarka/image_thumb_1CE0A22E.png" width="202" height="244" /></a> <a href="http://sqlblog.com/blogs/dejan_sarka/image_1F86CD2E.png"><img title="image" style="border-top:0px;border-right:0px;border-bottom:0px;border-left:0px;display:inline;" border="0" alt="image" src="http://sqlblog.com/blogs/dejan_sarka/image_thumb_1F1A9A39.png" width="136" height="52" /></a><a href="http://sqlblog.com/blogs/dejan_sarka/image_3C234901.png"><img title="image" style="border-top:0px;border-right:0px;border-bottom:0px;border-left:0px;display:inline;" border="0" alt="image" src="http://sqlblog.com/blogs/dejan_sarka/image_thumb_131B3AB6.png" width="202" height="244" /></a> </p> <p><a href="http://sqlblog.com/blogs/dejan_sarka/image_1F86CD2E.png"><img title="image" style="border-top:0px;border-right:0px;border-bottom:0px;border-left:0px;display:inline;" border="0" alt="image" src="http://sqlblog.com/blogs/dejan_sarka/image_thumb_1F1A9A39.png" width="136" height="52" /></a></p> <p><a href="http://sqlblog.com/blogs/dejan_sarka/image_753CB6B4.png"><img title="image" style="border-top:0px;border-right:0px;border-bottom:0px;border-left:0px;display:inline;" border="0" alt="image" src="http://sqlblog.com/blogs/dejan_sarka/image_thumb_2970DFFB.png" width="202" height="244" /></a> <a href="http://sqlblog.com/blogs/dejan_sarka/image_1F86CD2E.png"><img title="image" style="border-top:0px;border-right:0px;border-bottom:0px;border-left:0px;display:inline;" border="0" alt="image" src="http://sqlblog.com/blogs/dejan_sarka/image_thumb_1F1A9A39.png" width="136" height="52" /></a><a href="http://sqlblog.com/blogs/dejan_sarka/image_1D6EEFC7.png"><img title="image" style="border-top:0px;border-right:0px;border-bottom:0px;border-left:0px;display:inline;" border="0" alt="image" src="http://sqlblog.com/blogs/dejan_sarka/image_thumb_7F906BC5.png" width="202" height="244" /></a> <a href="http://sqlblog.com/blogs/dejan_sarka/image_1F86CD2E.png"><img title="image" style="border-top:0px;border-right:0px;border-bottom:0px;border-left:0px;display:inline;" border="0" alt="image" src="http://sqlblog.com/blogs/dejan_sarka/image_thumb_1F1A9A39.png" width="136" height="52" /></a><a href="http://sqlblog.com/blogs/dejan_sarka/image_1D718078.png"><img title="image" style="border-top:0px;border-right:0px;border-bottom:0px;border-left:0px;display:inline;" border="0" alt="image" src="http://sqlblog.com/blogs/dejan_sarka/image_thumb_51A5A9BE.png" width="202" height="244" /></a> </p> <p>Cluster analysis segments a heterogeneous population into a number of more homogenous subgroups or clusters. Typical usage scenarios include:</p> <ul> <li>Discovering distinct groups of customers</li> <li>Identifying groups of houses in a city</li> <li>In biology, deriving animal and plant taxonomies</li> <li>Can even make predictions once the clusters are built and distribution of a target variable in the clusters is calculated. </li> </ul>Off to Richmond for SQL Saturdayhttp://sqlblog.com/blogs/louis_davidson/archive/2015/03/13/off-to-richmond-for-sql-saturday.aspxFri, 13 Mar 2015 22:12:00 GMT21093a07-8b3d-42db-8cbf-3350fcbf5496:58188drsql<P class=MsoNormal style="MARGIN:0in 0in 8pt;"><FONT size=3><FONT face=Calibri>I got the email prodding speakers to blog about our upcoming sessions, so I got myself up and started to write this blog. It has been such a long time since I have done much side SQL work (other than doing quite a bit of tech editing, along with doing some work for PASS leading up to speaker submissions), that my blog didn't even show up in the blogs list on sqlblog.com. My last blog was right after PASS when I had attended the Summit from my hospital bed.<o:p></o:p></FONT></FONT></P>
<P class=MsoNormal style="MARGIN:0in 0in 8pt;"><FONT size=3><FONT face=Calibri>Since then, it has been quite a bumpy road. For a person who usually travels as much as I do for fun (SQL and Disney) and work, not having left the Nashville area since vacation in September has been weird. But all sorts of stuff have gotten in the way, mostly that I just haven't felt like blogging (heck, I haven't had an entry on my simple-talk blog since then either, though a few editorials were posted on sqlservercentral.com by my editor that I wrote pre-surgery).<o:p></o:p></FONT></FONT></P>
<P class=MsoNormal style="MARGIN:0in 0in 8pt;"><FONT size=3 face=Calibri>But now, finally, it is time to wake the heck up. I am leaving Nashville this Sunday, heading for a week of work in Virginia Beach, not coincidentally the same week as </FONT><A href="https://www.sqlsaturday.com/381/eventhome.aspx"><FONT color=#0563c1 size=3 face=Calibri>SQL Saturday Richmond</FONT></A><FONT size=3 face=Calibri> where I will be talking about In-Memory OLTP tables and how they affect your database design.<SPAN style="mso-spacerun:yes;">&nbsp; </SPAN>It is also pretty cool that </FONT><A href="https://twitter.com/jessicammoss"><FONT color=#0563c1 size=3 face=Calibri>Jessica Moss</FONT></A><FONT size=3 face=Calibri> will be presenting down in Virginia Beach while I am in town, so a stop at the </FONT><A href="http://hrssug.thecloudlyfe.com/"><FONT color=#0563c1 size=3 face=Calibri>Hampton Roads SQL Server User Group</FONT></A><FONT size=3><FONT face=Calibri> is definitely in order for most of the people I work with.<o:p></o:p></FONT></FONT></P>
<P class=MsoNormal style="MARGIN:0in 0in 8pt;"><FONT size=3 face=Calibri>Here is the abstract for my presentation (if you want Jessica’s go </FONT><A href="http://hrssug.thecloudlyfe.com/march-meeting-2015/"><FONT color=#0563c1 size=3 face=Calibri>here</FONT></A><FONT size=3><FONT face=Calibri>):</FONT></FONT></P>
<P class=MsoNormal style="MARGIN:0in 0in 8pt;">&nbsp;</P>
<P class=MsoNormal style="MARGIN:0in 0in 8pt;"><B style="mso-bidi-font-weight:normal;"><A href="https://www.sqlsaturday.com/viewsession.aspx?sat=381&amp;sessionid=28084"><FONT color=#0563c1 size=3 face=Calibri>How In-Memory Database Objects Affect Database Design</FONT></A><FONT size=3><FONT face=Calibri> <o:p></o:p></FONT></FONT></B></P>
<P class=MsoNormal style="MARGIN:0in 0in 8pt;"><FONT size=3><FONT face=Calibri>With SQL Server 2014, Microsoft has added a major new feature to help optimize OLTP database implementations by persisting your data primarily in RAM. Of course it isn't that simple, internally everything that uses this new feature is completely new. While the internals of this feature may be foreign to you, accessing the data that uses the structures very much resembles T-SQL as you already know it. As such, the first important question for the average developer will be how to adapt an existing application to make use of the technology to achieve enhanced performance. In this session, I will start with a normalized database, and adapt the logical and physical database model/implementation in several manners, performance testing the tables and code changes along the way.<o:p></o:p></FONT></FONT></P>
<P class=MsoNormal style="MARGIN:0in 0in 8pt;"><o:p><FONT size=3 face=Calibri>&nbsp;</FONT></o:p></P>
<P class=MsoNormal style="MARGIN:0in 0in 8pt;"><FONT size=3 face=Calibri>Does this mean I am fully back and over my funk? Good grief, I don't know. But I have submitted for 4 other SQL Saturdays over the rest of this year, and I have projects that are just waiting for me to get started. Some days I want to just lay down and not get up until it is time to go back to bed. Others I want to write a new book, travel to the ends of the earth and talk about SQL Server. The fact is, I am taking this one task at a time, and I look forward to talking about SQL Server for you at 9:45 on the 21st of March. And when that is over, I am going to Dollywood's opening weekend and let the </FONT><A href="http://www.dollywood.com/themepark/rides/Tennessee-Tornado.aspx"><FONT color=#0563c1 size=3 face=Calibri>Tennessee Tornado</FONT></A><FONT size=3 face=Calibri> spin some sense into my head. Hope I see you there (SQL Saturday or Dollywood, either way we can have some fun!)</FONT><o:p></o:p></P>SQLRally Nordic - after conference thoughtshttp://sqlblog.com/blogs/damian_widera/archive/2015/03/06/sqlrally-nordic-after-conference-thoughts.aspxFri, 06 Mar 2015 08:42:00 GMT21093a07-8b3d-42db-8cbf-3350fcbf5496:58124Damian Widera
<p>Hello All</p>
<p>I was extremely honored to be able to participate in the SQLRally Nordic this year. The event took place this week in Copenhagen and I think it was a great success - from every angle. Many thanks to the organizers and many thanks to the community for being in that place. There was a great atmosphere during the whole event and what I like the most as a speaker - I had a great room for my speech!&nbsp;</p>
<p>I hope that the event will take place also next years but now I would like to invite to the annual PLSSUG conference to Wrocław. Let's meet in May. At least half of the tracks will be in English and you could meet also may world-class speakers. Just visit the page <a mce_href="http://sqlday.pl" href="http://sqlday.pl">http://sqlday.pl</a></p>
<p>&nbsp;</p>
<p>Cheers</p>
<p>Damian&nbsp;</p>Data Mining Algorithms – an Introductionhttp://sqlblog.com/blogs/dejan_sarka/archive/2015/02/19/data-mining-algorithms-an-introduction.aspxThu, 19 Feb 2015 18:08:58 GMT21093a07-8b3d-42db-8cbf-3350fcbf5496:57938Dejan Sarka<p>Data mining is the most advanced part of business intelligence. With statistical and other mathematical algorithms, you can automatically discover patterns and rules in your data that are hard to notice with on-line analytical processing and reporting. However, you need to thoroughly understand how the data mining algorithms work in order to interpret the results correctly. In this blog I am introducing the data mining, and in the following blogs I am unveiling the black box of data mining and explaining how the most popular algorithms work.</p> <h3>Data Mining Definition</h3> <p>Data mining is a process of exploration and analysis, by automatic or semiautomatic means, of historical data in order to discover patterns and rules, which can be used later on new data for predictions and forecasting. With data mining, you deduce some hidden knowledge by examining, or training, the data. The unit of examination is called a <i>case</i>, which can be interpreted as one appearance of an entity, or a row, in a table. The knowledge is <i>patterns</i> and <i>rules</i>. In the process, you use attributes of a case, which are called <i>variables</i> in data mining terminology. For better understanding, you can compare data mining to On-Line Analytical Processing (OLAP), which is a model-driven analysis where you build the model in advance. Data mining is a data-driven analysis, where you search for the model. You examine the data with data mining algorithms.</p> <p>There are many alternative names for data mining, such as knowledge discovery in databases (KDD) and predictive analytics. Originally, data mining was not the same as machine learning in that it gives business users insights for actionable decisions; machine learning determines which algorithm performs the best for a specific task. However, nowadays data mining and machine learning are in many cases used as synonyms.</p> <h3>The Two Types of Data Mining</h3> <p>Data mining techniques are divided into two main classes:</p> <ul> <li>The <i>directed</i>, or <i>supervised</i> approach: You use known examples and apply gleaned information to unknown examples to predict selected target variable(s). </li> <li>The <i>undirected</i>, or <i>unsupervised</i> approach: You discover new patterns inside the dataset as a whole. </li> </ul> <p>Some of the most important directed techniques include classification, estimation, and forecasting. Classification means to examine a new case and assign it to a predefined discrete class. Examples are assigning keywords to articles and assigning customers to known segments. Very similar is estimation, where you are trying to estimate a value of a variable of a new case in a continuously defined pool of values. You can, for example, estimate the number of children or the family income. Forecasting is somewhat similar to classification and estimation. The main difference is that you can’t check the forecasted value at the time of the forecast. Of course, you can evaluate it if you just wait long enough. Examples include forecasting which customers will leave in the future, which customers will order additional services, and the sales amount in a specific region at a specific time in the future.</p> <p>The most common undirected techniques are clustering and affinity grouping. An example of clustering is looking through a large number of initially undifferentiated customers and trying to see if they fall into natural groupings. This is a pure example of &quot;undirected data mining&quot; where the user has no preordained agenda and hopes that the data mining tool will reveal some meaningful structure. Affinity grouping is a special kind of clustering that identifies events or transactions that occur simultaneously. A well-known example of affinity grouping is market basket analysis. Market basket analysis attempts to understand what items are sold together at the same time.</p> <h3>Common Business Use Cases</h3> <p>Some of the most common business questions that you can answer with data mining include:</p> <ul> <li>What’s the credit risk of this customer? </li> <li>Are there any groups of my customers? </li> <li>What products do customers tend to buy together? </li> <li>How much of a specific product can I sell in the next time period? </li> <li>What is the potential number of customers shopping in this store? </li> <li>What are the major groups of my web-click customers? </li> <li>Is this a spam email? </li> </ul> <p>However, the actual questions you might want to answer with data mining could be by far broader and depend on your imagination only. For an unconventional example, you might use data mining to try to lower the mortality rate in a hospital.</p> <p>Data mining is already widely used in many different applications. Some of the typical usages, along with the most commonly used algorithms for a specific task, include the following:</p> <ul> <li><i>Cross-selling</i>: Widely used for web sales with the Association Rules and Decision Trees algorithms. </li> <li><i>Fraud detection</i>: An important task for banks and credit card issuers, who want to limit the damage that fraud creates, including that experienced by customers and companies. The Clustering and Decision Trees algorithms are commonly used for fraud detection. </li> <li><i>Churn detection</i>: Service providers, including telecommunications, banking, and insurance companies, perform this to detect which of their subscribers are about to leave them in an attempt to prevent it. Any of the directed methods, including the Naive Bayes, Decision Trees, or Neural Network algorithm, is suitable for this task. </li> <li><i>Customer Relationship Management (CRM) applications</i>: Based on knowledge about customers, which you can extract with segmentation using, for example, the Clustering or Decision Trees algorithm. </li> <li><i>Website optimization</i>: To do this, you should know how your website is used. Microsoft developed a special algorithm, the Sequence Clustering algorithm, for this task. </li> <li><i>Forecasting</i>: Nearly any business would like to have some forecasting, in order to prepare better plans and budgets. The Time Series algorithm is specially designed for this task. </li> </ul> <h3>A Quick Introduction to the Most Popular Algorithms</h3> <p>In order to raise the expectations for the upcoming blogs, I am adding a brief introduction to the most popular data mining algorithms in a condensed way, in a table. <table cellspacing="0" cellpadding="0"> <tr> <td> <p><strong>Algorithm</strong></p> </td> <td> <p><strong>Usage</strong></p> </td> </tr> <tr> <td> <p>Association Rules</p> </td> <td> <p>The algorithm used for market basket analysis, this defines an itemset as a combination of items in a single transaction. It then scans the data and counts the number of times the itemsets appear together in transactions. Market basket analysis is useful to detect cross-selling opportunities.</p> </td> </tr> <tr> <td> <p>Clustering</p> </td> <td> <p>This groups cases from a dataset into clusters containing similar characteristics. You can use the Clustering method to group your customers for your CRM application to find distinguishable groups of your customers. In addition, you can use it for finding anomalies in your data. If a case does not fit well to any cluster, it is kind of an exception. For example, this might be a fraudulent transaction.</p> </td> </tr> <tr> <td> <p>Naïve Bayes</p> </td> <td> <p>This calculates probabilities for each possible state of the input attribute for every single state of predictable variable. Those probabilities predict the target attribute based on the known input attributes of new cases. The Naïve Bayes algorithm is quite simple; it builds the models quickly. Therefore, it is very suitable as a starting point in your predictive analytics project. </p> </td> </tr> <tr> <td> <p>Decision Trees </p> </td> <td> <p>The most popular DM algorithm, it predicts discrete and continuous variables. It uses the discrete input variables to split the tree into nodes in such a way that each node is more pure in terms of target variable, i.e. each split leads to nodes where a single state of a target variable is represented better than other states.</p> </td> </tr> <tr> <td> <p>Regression Trees</p> </td> <td> <p>For continuous predictable variables, you get a piecemeal multiple linear regression formula with a separate formula in each node of a tree. Discrete input variables are used to split the tree into nodes. A tree that predicts continuous variables is a Regression Tree. Use Regression Trees for estimation of a continuous variable; for example, a bank might use this technique to estimate the family income for a loan applicant.</p> </td> </tr> <tr> <td> <p>Linear Regression</p> </td> <td> <p>Predicts continuous variables, using a single multiple linear regression formula. The input variables must be continuous as well. Linear Regression is a simple case of a Regression Tree, a tree with no splits. Use it for the same purpose as Regression Trees.</p> </td> </tr> <tr> <td> <p>Neural Network</p> </td> <td> <p>This algorithm is from artificial intelligence, but you can use it for predictions as well. Neural networks search for nonlinear functional dependencies by performing nonlinear transformations on the data in layers, from the input layer through hidden layers to the output layer. Because of the multiple nonlinear transformations, neural networks are harder to interpret compared to Decision Trees.</p> </td> </tr> <tr> <td> <p>Logistic Regression</p> </td> <td> <p>As Linear Regression is a simple Regression Tree, a Logistic Regression is a Neural Network without any hidden layers.</p> </td> </tr> <tr> <td> <p align="left">Support Vector Machines</p> </td> <td> <p>Support Vector Machines are supervised learning models with associated learning algorithms that analyse data and recognize patterns, used for classification. A support vector machine constructs a hyper plane or set of hyper planes in a high-dimensional space where the input variables define the dimensions. The hyper planes split the data points into discrete groups of the target variable. Support Vector Machines are powerful for some specific classifications, like text and images classifications and hand-written characters recognition.</p> </td> </tr> <tr> <td> <p>Sequence Clustering</p> </td> <td> <p>This searches for clusters based on a model, and not on similarity of cases as Clustering does. The models are defined on sequences of events by using Markov Chains. Typical usage of the Sequence Clustering would be an analysis of your company’s Web site usage, although you can use this algorithm on any sequential data.</p> </td> </tr> <tr> <td> <p>Time Series</p> </td> <td> <p>You can use this algorithm to forecast continuous variables. Time Series many times denotes two different internal algorithms. For short-term forecasting, Auto-Regression Trees (ART) algorithm is used. For long-term prediction, Auto-Regressive Integrated Moving Average (ARIMA) algorithm is used. </p> </td> </tr> </table> </p> <h3>Conclusion</h3> <p>This brief introduction to data mining should give you the idea what you could use it for and an overview which algorithms are appropriate for the business problem you are trying to solve. I guess you also noticed I am not talking about any specific technology here. These most popular data mining algorithms are available in many different products. For example, you can find them in SQL Server Analysis Services, Excel with Data Mining Add-ins, R, Azure ML, and more. Please learn how to use them with your specific product using the documentation of the product, by reading books that deal with your product, or by visiting a course about the product.</p> <p>I hope you got excited enough to read the upcoming blogs and visit some of my presentations on various conferences.</p>T-SQL Queryinghttp://sqlblog.com/blogs/dejan_sarka/archive/2015/02/16/t-sql-querying.aspxMon, 16 Feb 2015 07:00:17 GMT21093a07-8b3d-42db-8cbf-3350fcbf5496:57881Dejan Sarka<p>We are close to the publishing day of the T-SQL Querying book. Of course, like always in this series, the main author of the book is Itzik Ben-Gan. This time, besides me, Adam Machanic and Kevin Farlee are the coauthors. The information I want to share now is that you can get a substantial discount if you preorder the book today, Monday, February 16th, 2015. Pearson is running the Presidents Day Event and giving the following discounts for this and some other products:</p> <ul> <li>Buy 1, Save 35%</li> <li>Buy 2, Save 50%</li> <li>Up to 70% off on featured video titles</li> </ul> <p>You can preorder the book using this <a href="http://click.linksynergy.com/fs-bin/click?id=pWec/DmrGUA&amp;offerid=145238&amp;type=3&amp;subid=0">link</a>. Once the page opens, just click the President’s Day Sale banner and select our or any other book on sale. </p> <p>Happy querying!</p>SQL Server 2012 SP2 Cumulative Update 4 Released - information from the SQL Server SE Operations Teamhttp://sqlblog.com/blogs/damian_widera/archive/2015/01/20/sql-server-2012-sp2-cumulative-update-4-released-information-from-the-sql-server-se-operations-team.aspxWed, 21 Jan 2015 01:46:00 GMT21093a07-8b3d-42db-8cbf-3350fcbf5496:57669Damian Widera<p class="MsoNormal" style="margin-bottom:0.0001pt;"><a name="_GoBack" class=""></a>As per
information from Microsoft:<o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:0.0001pt;">"It is my pleasure to announce
the release of SQL Server 2012 SP2 Cumulative Update 4 on behalf of the
team.&nbsp; SQL Server 2012 SP2 Cumulative Update 4 incorporates 42 issues.<o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:0.0001pt;">This CU will be available for download
from the associated cumulative KB article that has also been published.&nbsp;
Customers are directed to contact CSS to get the CU build or obtain the hotfix
package through the new self-service feature by clicking on the “Hotfix
Download Available” button found at the top of the KB article."<o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:0.0001pt;"><span style="font-size:10pt;">To me the most important fix is the
one regarding the possibility of having errors 17066 or 17310 during SQL Server
startup (immediately after database recovery is complete and client connections
are enabled.). Check out the link&nbsp;http://support.microsoft.com/kb/3027860
for details</span></p><p class="MsoNormal" style="margin-bottom:0.0001pt;"><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:0.0001pt;"><span style="font-size:10pt;">Link to the official site of this
update:&nbsp;http://support.microsoft.com/kb/3007556/en-us</span></p><p class="MsoNormal" style="margin-bottom:0.0001pt;"><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:0.0001pt;"><span style="font-size:10pt;">Cheers</span></p><p class="MsoNormal" style="margin-bottom:0.0001pt;"><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:0.0001pt;">Damian&nbsp;<o:p></o:p></p>
<p class="MsoNormal"><o:p>&nbsp;</o:p></p>Getting Started with SSIS: Get the Software and Toolshttp://sqlblog.com/blogs/andy_leonard/archive/2015/01/01/getting-started-with-ssis-get-the-software-and-tools.aspxThu, 01 Jan 2015 12:01:00 GMT21093a07-8b3d-42db-8cbf-3350fcbf5496:56893andyleonard
<p>People often ask me for tips on getting started with SSIS. My advice is always the same: Get a copy of SQL Server Developer Edition. At the time of this writing, the current version of SQL Server is SQL Server 2014. If you work in an organization that uses SQL Server, you may be able to obtain a copy of SQL Server 2014 Developer Edition from your organization. If not, I highly recommend purchasing your own copy. </p>
<p><a mce_href="http://www.amazon.com/Server-Developer-2014-English-Only/dp/B00JKMY8KC" target="_blank" href="http://www.amazon.com/Server-Developer-2014-English-Only/dp/B00JKMY8KC">SQL Server 2014 Developer Edition</a> is available at Amazon.com for about $50 USD. Developer Edition has all the features of Enterprise Edition, only the End User License Agreement is different – you cannot use Developer Edition to operate a Production instance.</p>
<p>You can begin using SQL Server 2014 for free by <a mce_href="http://www.microsoft.com/en-us/evalcenter/evaluate-sql-server-2014" target="_blank" href="http://www.microsoft.com/en-us/evalcenter/evaluate-sql-server-2014">downloading the 180-day Evaluation Edition</a>.</p>
<p>Once you have an edition of SQL Server installed, you will need SQL Server Data Tools – Business Intelligence (SSDT-BI) to develop SSIS packages. SSDT-BI is no longer included with the SQL Server installation media. The current version at the time of this writing is SSDT-BI 2013 and you can download it <a mce_href="http://www.microsoft.com/en-us/download/details.aspx?id=42313" target="_blank" href="http://www.microsoft.com/en-us/download/details.aspx?id=42313">here</a>.</p>
<p>Once the relational engine and development tools are installed, you are ready to begin working with SSIS!</p>
<p>Learn more:
<br><a target="_blank" href="http://www.linchpinpeople.com/getting-started-ssis-video/">Watch the video!</a>
<br><a target="_blank" href="http://www.linchpinpeople.com/tag/ssis/">Linchpin People Blog: SSIS</a>
<br><a target="_blank" href="http://www.sqlservercentral.com/stairway/72494/">Stairway to Integration Services</a>
<br><a target="_blank" href="http://quizegg.com/q/91506">Test your knowledge</a></p>
<p><span style="font-size:10pt;">:{&gt;</span></p>PASS SQL Saturday #356 Slovenia Recapitulationhttp://sqlblog.com/blogs/dejan_sarka/archive/2014/12/15/pass-sql-saturday-356-slovenia-recapitulation.aspxMon, 15 Dec 2014 11:28:11 GMT21093a07-8b3d-42db-8cbf-3350fcbf5496:57402Dejan Sarka<p>So the <a href="https://sqlsaturday.com/356/eventhome.aspx">event</a> is over. I think I can say for all three organizers, <a href="https://twitter.com/MladenPrajdic">Mladen Prajdić</a>, <a href="https://twitter.com/MatijaLah">Matija Lah</a>, and <a href="https://twitter.com/DejanSarka">me</a>, that we are tired now. However, we are extremely satisfied. It was a great event. First few numbers and comparison with <a href="https://sqlsaturday.com/274/eventhome.aspx">SQL Saturday #274</a>, the first SQL Saturday Slovenia event that took place last year.</p> <table cellspacing="0" cellpadding="2"> <tr> <td> <p align="center"></p> </td> <td> <p align="center">SQL Saturday #274</p> </td> <td> <p align="center">SQL Saturday #356</p> </td> </tr> <tr> <td> <p align="center">People</p> </td> <td> <p align="center">135</p> </td> <td> <p align="center">220</p> </td> </tr> <tr> <td> <p align="center">Show rate</p> </td> <td> <p align="center">~87%</p> </td> <td> <p align="center">~95%</p> </td> </tr> <tr> <td> <p align="center">Proposed sessions</p> </td> <td> <p align="center">40</p> </td> <td> <p align="center">82</p> </td> </tr> <tr> <td> <p align="center">Selected sessions</p> </td> <td> <p align="center">15</p> </td> <td> <p align="center">24</p> </td> </tr> <tr> <td> <p align="center">Selected speakers</p> </td> <td> <p align="center">14</p> </td> <td> <p align="center">23</p> </td> </tr> <tr> <td> <p align="center">Countries</p> </td> <td> <p align="center">12</p> </td> <td> <p align="center">16</p> </td> </tr> </table> <p>The numbers nearly doubled. We are especially proud of the show rate; with 95%, this is much better than average for a free event, and probably the highest so far for a SQL Saturday. We asked registered attendees to be fair and to unregister if they know they can’t attend the event in order to make room for those from the waiting list. An old Slovenian proverb says “A nice word finds a nice place”, and it works. 36 registered attendees unregistered. Therefore, we have to thank to both, the attendees of the event and those who unregistered.</p> <p>Of course, as always, we also need to thank to all of the speakers, sponsors and volunteers. All volunteers were very helpful; however, I would like to especially point out <a href="https://twitter.com/Firbec">Saša Mašič</a>. Her work goes well beyond simple volunteering. I must mention also the <a href="http://www.fri.uni-lj.si/en/">FRI</a>, the Faculty of Computer and Information Science, where the event was hosted for free. It is also worth mentioning that we are lucky to live in <a href="http://www.visitljubljana.com/">Ljubljana</a>, such a beautiful city with extremely nice inhabitants who like to enjoy good food, hanging around and mingling, and long parties. Because of that we could be sure in advance that both speakers and attendees from other countries would enjoy spending time here also outside the event, that they would feel safe, and get help whenever they would need it.</p> <p>From the organizational perspective, we tried to do our best, and we hope that everything was OK for speakers, sponsors, volunteers, and attendees. Thank you all!</p>Columnstore Indexes in SQL Server 2014: Flipping the DW /faster Bit at #SQLSaturday347 / #SQLSatDChttp://sqlblog.com/blogs/jimmy_may/archive/2014/12/05/columnstore-indexes-in-sql-server-2014-flipping-the-dw-faster-bit-at-sqlsaturday349-sqlsatdc.aspxFri, 05 Dec 2014 20:14:00 GMT21093a07-8b3d-42db-8cbf-3350fcbf5496:56917aspiringgeek<p>This weekend on December 6, 2014 I continue my evangelization of Columnstore—possibly the most exciting unheralded feature in SQL Server 2014—at SQL Saturday 347 at an auspicious venue: the Microsoft Technology Center in Chevy Chase, proximal to the US capital city of Washington DC.</p>
<p><a href="https://www.sqlsaturday.com/347/register.aspx" mce_href="https://www.sqlsaturday.com/347/register.aspx">Register</a>, see the <a href="https://www.sqlsaturday.com/347/schedule.aspx" mce_href="https://www.sqlsaturday.com/347/schedule.aspx">schedule</a>, or see the <a href="https://www.sqlsaturday.com/347/eventhome.aspx" mce_href="https://www.sqlsaturday.com/347/eventhome.aspx">event home page</a> on the SQL Saturday site.&nbsp; I’ll look forward to seeing you here:</p>
<blockquote>
<p>Microsoft MTC <br>5404 Wisconsin Ave <br>Chevy Chase, MD 20815</p>
</blockquote>
<p>Join me &amp; an All-Star cast of speakers including MVPs such as <a href="https://twitter.com/SQLReeves" mce_href="https://twitter.com/SQLReeves">Reeves Smith</a>, <a href="https://twitter.com/jdanton" mce_href="https://twitter.com/jdanton">Joey D’Antoni</a>, <a href="https://twitter.com/sqlrunr" mce_href="https://twitter.com/sqlrunr">Allen White</a>, <a href="https://twitter.com/jessicammoss" mce_href="https://twitter.com/jessicammoss">Jessica Moss</a>, <a href="https://twitter.com/gfritchey" mce_href="https://twitter.com/gfritchey">Grant Fitchey</a>, <a href="https://twitter.com/way0utwest" mce_href="https://twitter.com/way0utwest">Steve Jones</a>, <a href="https://twitter.com/randy_knight" mce_href="https://twitter.com/randy_knight">Randy Knight</a>, <a href="https://twitter.com/sqlrnnr" mce_href="https://twitter.com/sqlrnnr">Jason Brimhall</a>, &amp; <a href="https://twitter.com/DBAWayne/" mce_href="https://twitter.com/DBAWayne/">Wayne Sheffield</a>.&nbsp; Other luminaries include shiny new Microsoft PFE <a href="https://twitter.com/TheresaIserman" mce_href="https://twitter.com/TheresaIserman">Theresa Iserman</a> &amp; Consultant <a href="https://twitter.com/thesqlpro" mce_href="https://twitter.com/thesqlpro">Ayman El-Ghazali</a>.</p>
<blockquote>
<p><img src="http://blogs.msdn.com/cfs-file.ashx/__key/communityserver-blogs-components-weblogfiles/00-00-01-05-28-metablogapi/1057.image_5F00_73742CB5.png"><a href="http://sqlblog.com/cfs-file.ashx/__key/communityserver-blogs-components-weblogfiles/00-00-01-05-28-metablogapi/1057.image_5F00_73742CB5.png" mce_href="http://sqlblog.com/cfs-file.ashx/__key/communityserver-blogs-components-weblogfiles/00-00-01-05-28-metablogapi/1057.image_5F00_73742CB5.png"></a></p>
</blockquote>It’s That Time of Year to Think About Your Careerhttp://sqlblog.com/blogs/andy_leonard/archive/2014/12/05/it-s-that-time-of-year-to-think-about-your-career.aspxFri, 05 Dec 2014 18:00:00 GMT21093a07-8b3d-42db-8cbf-3350fcbf5496:56918andyleonard<p>If you work with SQL Server (or <em>want</em> to work with SQL Server), this is a good time of the year to think about your career. Why? It’s the holiday season and you should have some time off… unless you have a <a href="http://sqlblog.com/blogs/andy_leonard/archive/2008/12/09/toughest-career-challenge.aspx" target="_blank">Crappy Job</a>.</p> <p>You can use some of this time to advance your knowledge about SQL Server, especially the new version: SQL Server 2014. How? You can download a free 180-day version <a href="http://www.microsoft.com/en-us/evalcenter/evaluate-sql-server-2014" target="_blank">here</a>. That version will carry you through the holidays and then some. Install it, poke around some, search for online tutorials (there are some good tutorials available <a href="http://msdn.microsoft.com/en-us/library/hh231699.aspx" target="_blank">at the Microsoft MSDN site</a>), and <em>learn</em>!</p> <p>o&lt;:{&gt;</p>